Systems and methods for predicting crop size and yield

ABSTRACT

Methods for predicting a yield of fruit growing in an agricultural plot are provided. At a first time, a first plurality of images of a canopy of the agricultural plot is obtained from an aerial view of the canopy of the agricultural plot. From the first plurality of images, a first number of detectable fruit is estimated. At a second time, a second plurality of images of the canopy of the agricultural plot is obtained from the aerial view of the canopy of the agricultural plot. From the second plurality of images, a second number of detectable fruit is estimated. Using at least the first number of detectable fruit and the second number of detectable fruit and agricultural plot information, predict the yield of fruit from the agricultural plot.

PRIORITY APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/206,064, filed Mar. 18, 2021, which is a continuation ofInternational Application PCT/US21/20038, filed Feb. 26, 2021, whichclaims priority to U.S. Provisional Application 62/988,213, filed Mar.11, 2020, each of which is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

This specification describes using aerial images obtained from anagricultural plot to estimate fruit sizes (e.g., estimate a distributionof fruit sizes), predict a crop yield (e.g., using estimated fruitsizes) or other characteristics of the agricultural plot.

BACKGROUND

A perennial issue across all agriculture is the accurate prediction offruit size and/or crop yield. Only through such estimates can farmersappropriately plan for the coming seasons (e.g., by determiningappropriate pricing of their produce and making revenue predictions,which in turn affect planning for the next year). In addition,determining crop yield and when the crop will be ripe are essential forefficient harvest logistics (e.g., with regards to hiring enough laborfor harvesting, negotiating fair contracts with downstream suppliers,and maximizing yield). Current methods of crop yield and/or fruit sizeprediction rely, in great part, on labor-intensive manual counting ofindividual fruit and often-inaccurate projections of crop yield fromprevious years.

Given the above background, robust techniques for accurately predictingfruit size and/or crop yield are needed in the art.

SUMMARY

The present disclosure addresses the shortcomings identified in thebackground by providing robust techniques for predicting crop yield(e.g., fruit yield) through estimations of crop (e.g., fruit) size. Withimproved methods of image analysis to detect individual crops anddetermine respective crop sizes, more accurate yield predictions arepossible. Further, determination of plant and crop health metrics canalso be performed via image analysis; these metrics can, in some cases,contribute to predicting crop size and/or crop yield. However, suchinformation can be used to improve overall farming efficiency (e.g.,determining whether growth conditions are optimal, if additionalfertilizer and/or water is needed, etc.).

Note that, while the remainder of this document refers often to fruitsize and yield, one of skill in the art having the benefit of thisdisclosure will understand that many of the systems and methodsdescribed herein are applicable to any crop, including non-fruit crops(lettuce, turnips, broccoli, etc.) Further, the term “fruit” should beconstrued to include the fruiting body of any plant. For example,walnuts, peppers, beans, and the like, are all fruit, as are oranges,apples, etc.

Further, although some embodiments of the present disclosure refer toimages obtained aerially (e.g., from a unmanned aerial vehicle or asatellite), in some embodiments, ground-based imagery can be used tosupplement or replace the aerial imagery. One of skill in the art havingthe benefit of this disclosure will understand to which embodimentsground-based imagery may be applicable.

I. Using Images from Multiple Time Points to Predict Fruit Yield and/orFruit Size.

One aspect of the present disclosure provides a method of predicting ayield of fruit growing in an agricultural plot. The method comprisesobtaining, at a first time, a first plurality of images of a canopy ofthe agricultural plot from an aerial view of the canopy of theagricultural plot. The method further includes estimating, from thefirst plurality of images of the canopy of the agricultural plotobtained from the aerial view of the canopy of the agricultural plot, afirst number of fruit detectable from the first plurality of images ofthe canopy of the agricultural plot. The method further comprisesobtaining, at a second time, a second plurality of images of the canopyof the agricultural plot from the aerial view of the canopy of theagricultural plot (note that the term “the aerial view” is notnecessarily intended to suggest that the first plurality of images andthe second plurality of images are obtained from the same vantage point;rather, the aerial view is simply over the agricultural plot). Themethod further comprises estimating, from the second plurality of imagesof the canopy of the agricultural plot obtained from the aerial view ofthe canopy of the agricultural plot, a second number of fruit detectablefrom the second plurality of images of the canopy of the agriculturalplot. The method further comprises using at least i) the first andsecond number of fruit detectable from the aerial view of the canopy ofthe agricultural plot and ii) contextual information for theagricultural plot, to predict the yield of fruit from the agriculturalplot.

II. Using Images from Multiple Locations to Predict Fruit Yield and/orFruit Size.

Another aspect of the present disclosure provides a method of predictinga yield of fruit growing in an agricultural plot. The method comprisesobtaining, using a camera, a first plurality of images of a canopy ofthe agricultural plot. For each respective fruit of a plurality of fruitgrowing in the agricultural plot, the method proceeds by identifying therespective fruit in a first respective image in the first plurality ofimages, wherein the first respective image has a corresponding firstcamera location, identifying the respective fruit in a second respectiveimage in the first plurality of images, wherein the second respectiveimage has a corresponding second camera location; and using at least i)the first and second respective images, and ii) a distance between thefirst and second camera locations, to determine a corresponding size ofthe respective fruit. In some embodiments, the method further comprisespredicting a yield of fruit growing in the agricultural plot based atleast in part on the plurality of fruit sizes.

III. Using Images with Different Resolutions to Predict Fruit Yieldand/or Fruit Size.

Another aspect of the present disclosure provides a method of predictinga yield of fruit growing in an agricultural plot. The method comprisesobtaining a plurality of images of a canopy of the agricultural plotfrom an aerial view of the canopy of the agricultural plot, where theplurality of images includes a first subset of images and a secondsubset of images. Each image in the first subset of images correspondsto a respective first portion of the agricultural plot. Each image inthe second subset of images corresponds to a respective second portionof the agricultural plot. The second portion of the agricultural plot issmaller than the first portion of the agricultural plot (e.g., thesecond portion is obtained from closer, or with a higher magnification).The method further includes obtaining a first route record correspondingto the first subset of images, and a second route record correspondingto the second subset of images. The first route record comprises i) afirst route over the agricultural plot for a first vehicle (e.g., anaircraft or an unmanned aerial vehicle such as a drone, a satellite, orother aircraft), and ii) a respective velocity of the first vehicle foreach image in the first subset of images. The second route recordcomprises i) a second route over the agricultural plot for a secondvehicle (e.g., an aircraft or an unmanned aerial vehicle such as adrone, a satellite, or other aircraft), and ii) a respective velocity ofthe second vehicle for each image in the second subset of images. Themethod further includes estimating, from the first subset of images ofthe canopy of the agricultural plot obtained from the aerial view of thecanopy of the agricultural plot, a number of fruit detectable from theplurality of images of the canopy of the agricultural plot. The methodproceeds by determining, from the second subset of images, for eachfruit of a plurality of fruit, a respective fruit size. The methodfurther comprises using the number of fruit detectable from the aerialview of the canopy of the agricultural plot and the plurality of fruitsizes, to predict the yield of fruit from the agricultural plot.

In accordance with some embodiments, the features described below areapplicable to any one or more of the methods described above.

In some embodiments, the first plurality of images of the canopy of theagricultural plot comprises images of a plurality of plants growing inthe agricultural plot. In some embodiments, the second plurality ofimages of the canopy of the agricultural plot comprises images of aplurality of plants growing in the agricultural plot.

In some embodiments, the method further comprises using at least a firstdepth of focus of the camera to determine a corresponding size of therespective fruit. In some embodiments, the method further comprisesusing at least a first depth of focus of the camera, a respective cameralocation (e.g., location from which the respective image is obtained bythe respective camera), a respective image resolution, and/or acombination thereof to determine a corresponding size of the respectivefruit.

In some embodiments, predicting the yield of fruit from the agriculturalplot further comprises estimating a respective yield of fruit for eachplant in the plurality of plants in the agricultural plot.

In some embodiments, the first plurality of images includes a firstsubset of images and a second subset of images. In some embodiments,each image in the first subset of images corresponds to a respectivefirst portion of the agricultural plot, and each image in the secondsubset of images corresponds to a respective second portion of theagricultural plot. In some embodiments, each image in the first subsetof images has a corresponding first resolution, and each image in thesecond subset of images has a corresponding second resolution, where thesecond resolution is higher than the first resolution. In someembodiments, the first subset of images is obtained at a first heightand the second subset of images is obtained from a second height.

In some embodiments, the second plurality of images includes a thirdsubset of images and a fourth subset of images. In some embodiments,each image in the third subset of images corresponds to the respectivefirst (or a respective third) portion of the agricultural plot, and eachimage in the fourth subset of images corresponds to the respectivesecond (or a respective fourth) portion of the agricultural plot. Insome embodiments, each image in the third subset of images has thecorresponding first (or a corresponding third) resolution, and eachimage in the fourth subset of images has the corresponding second (or acorresponding fourth) resolution, where the second (and/or fourth)resolution is higher than the first (and/or third) resolution. In someembodiments, the third subset of images is obtained at the first heightand the fourth subset of images is obtained from the second height

In some embodiments, the second portion of the agricultural plot issmaller than the first portion of the agricultural plot. In someembodiments, each of the first portion, the second portion, the thirdportion, and the fourth portion are different portions of theagricultural plot. In some embodiments, the first portion and the thirdportion are substantially the same portion (e.g., within the accuracy ofthe ability to control the respective vehicle, as described below). Insome embodiments, the second portion and the fourth portion aresubstantially the same portion.

In some embodiments, resolution corresponds to a level of detail in arespective image (e.g., a high-resolution image corresponds to an imagewith a high-level of detail). In some embodiments, “resolution” as usedherein refers to a minimum distance within an object plane (e.g., theplane of the trees/fruit) for which two points can be resolved. In someembodiments, the second resolution is different from the firstresolution. In some embodiments, each of the first resolution, thesecond resolution, the third resolution, and the fourth resolution aredifferent image resolutions. In some embodiments, the first resolutionand the third resolution are substantially the same resolution (e.g.,within the accuracy of the ability to control the respective vehicle, asdescribed below). In some embodiments, the second resolution and thefourth resolution are substantially the same resolution.

In some embodiments, the first height is above the agricultural plot. Insome embodiments, the second height is different from the first height.In some embodiments, each of the first height, the second height, thethird height, and the fourth height are different heights. In someembodiments, the first height and the third height are substantially thesame height (e.g., within the accuracy of the ability to control therespective vehicle, as described below). In some embodiments, the secondheight and the fourth height are substantially the same height. In someembodiments, height corresponds to a distance above the ground of acamera obtaining the first and/or second pluralities of images.

In some embodiments, the method further comprises determining from thesecond subset of images, for each fruit of a plurality of fruit growingon a subset of plants, a respective fruit size. In some embodiments, themethod further comprises determining from the fourth subset of images,for each fruit of a plurality of fruit growing on a subset of plants, arespective fruit size.

In some embodiments, the method further comprises scaling (e.g.,extrapolating from) the predicted yield of fruit from the agriculturalplot by the plurality of fruit sizes (e.g., where the predicted yield offruit is provided in an amount such as weight (e.g., tonnage) or volume(e.g., bushels)). In some embodiments, the method further comprisesdetermining, for each fruit in the plurality of fruit, a respectivefruit weight based on the corresponding fruit size, and scaling thepredicted yield of fruit by the plurality of fruit weights. In someembodiments, the scaling is non-linear.

In some embodiments, the method further comprises obtaining historicalyield information and/or contextual information for the agriculturalplot, and scaling the predicted yield of fruit from the agriculturalplot using the historical yield or contextual information. In someembodiments, the contextual information for the agricultural plotcomprises historical yield information for the agricultural plot or fora similar or surrounding region. In some embodiments, the contextualinformation for the agricultural plot comprises near infrared (or othernarrow band sensor) information obtained for the agricultural plot,temperature information obtained (e.g., via sensors) for theagricultural plot, humidity information obtained for the agriculturalplot, or a combination thereof.

In some embodiments, the method further comprises estimating for eachplant detected in the first plurality of images, a respective number offruit and, for each fruit growing in each plant, a corresponding fruitsize. In some embodiments, the method further comprises estimating foreach plant in the plurality of plants, a respective number of fruit and,for each fruit growing in each plant a corresponding fruit size.

In some embodiments, the method further comprises scaling the predictedyield of fruit from the agricultural plot using the respective number offruit and the corresponding fruit sizes for each plant in the pluralityof plants. In some embodiments, the scaling is non-linear.

In some embodiments, the method further comprises using a first vehicleto obtain the first plurality of images and a second vehicle to obtainthe second plurality of images. In some embodiments, the method furthercomprises obtaining a first route record for the first vehiclecorresponding to the first plurality of images, and a second routerecord for the second vehicle corresponding to the second plurality ofimages. In some embodiments, the first vehicle is the second vehicle. Insome embodiments, the first vehicle and the second vehicle are differentvehicles.

In some embodiments, the first and/or second vehicle comprises an aerialvehicle such as a manned or unmanned aerial vehicle (UAV). In someembodiments, the UAV comprises a drone, a satellite, or anotheraircraft.

In some embodiments, the first route record comprises i) a first routeover the agricultural plot and ii) a respective velocity of the firstvehicle for each image in the first plurality of images, and the secondroute record comprises i) a second route over the agricultural plot andii) a respective velocity of the second vehicle for each image in thesecond plurality of images. In some embodiments, the first route recordfurther comprises iii) a respective timestamp for each image in thefirst plurality of images (e.g., where each respective timestampcorresponds to a time an image was obtained). In some embodiments, thesecond route record further comprises iii) a respective timestamp in thesecond plurality of images.

In some embodiments, the first route record further comprises arespective height and a respective location for each image in the firstsubset (and/or first plurality) of images, and each image in the firstsubset (and/or first plurality) of images is evaluated for satisfactionof one or more validation criteria.

In some embodiments, the second route record further comprises arespective height and a respective location for each image in the secondsubset (and/or second plurality) of images, and each image in the secondsubset (and/or second plurality) of images is evaluated for satisfactionof one or more validation criteria.

In some embodiments, the method further comprises estimating, from thefirst subset of images, a respective plant size of each respective plantof a plurality of plants in the agricultural plot, and estimating, fromthe second subset of images, for each fruit of a plurality of fruitgrowing in the canopy of the agricultural plot, a respective fruit size.In some embodiments, the plurality of fruit comprises fruit growing onplants in a respective portion of the agricultural plot.

In some embodiments, the method further comprises displaying a histogramof fruit size using the fruit sizes for the plurality of fruit and theplant sizes for the plurality of plants. In some embodiments, the methodfurther comprises displaying a histogram of fruit color (e.g., binningfruits by color shade, which can be indicative of ripeness). In someembodiments, the method further comprises displaying a histogram offruit maturity stages (e.g., as determined by fruit size, fruit color,or a combination thereof).

In some embodiments, the first and/or second vehicle (e.g., the firstand/or second UAV) includes an RGB camera. In some embodiments, eachimage in the first and second subsets of images comprises acorresponding RGB image. In some embodiments, each image in theplurality of images comprises an RGB image.

In some embodiments, the fruit are of a fruit type selected from thegroup consisting of blueberries, cherries, plums, peaches, nectarines,apricots, olives, mangos, pears, apples, quinces, loquats, citrus, figs,papayas, avocados, coconuts, durians, guavas, persimmons, pomegranates,nuts, or the like. In other embodiments, the inventions can be used toidentify and estimate any object that can be identified from above,including people, animals, structures, vehicles, or the like.

In some embodiments, the method is repeated at one or more time pointsover a season (e.g., a respective growing season for a respective fruittype) to obtain updates of the predicted yield of fruit.

In some embodiments, the method further comprises providing a confidencevalue for the predicted yield of fruit from the agricultural plot.

In some embodiments, the second subset of images of the canopy of theagricultural plot includes two or more images obtained at differentpositions in the agricultural plot, where the two or more images includea respective fruit growing in the agricultural plot.

In some embodiments, the method further comprises, for each respectivefruit of the plurality of fruit, using a first trained computationalmodel (e.g., a machine learning model) applied to the two or more imagesthat include a respective fruit, identifying a corresponding contour ofthe respective fruit in the respective two or more images, therebyobtaining respective contours of the same fruit in the two or moreimages, and calculating a size of the respective fruit from therespective contours of the respective fruit in the two or more images.In some embodiments, calculating the size of the respective fruitfurther comprises using stereotriangulation as described with regards toFIGS. 8A-8C.

In some embodiments, calculating the respective fruit size for eachfruit in the plurality of fruit includes assigning a respective fruitidentifier for each fruit in the plurality of fruit. In someembodiments, the calculating further comprises averaging the size ofeach respective fruit across the plurality of images (e.g., for each setof two or more images).

In some embodiments, a second trained computational model is used toverify that each contour corresponds to a fruit, and contours that donot correspond to a fruit (e.g., contours that delineate leaves or otherportions of the canopy of the agricultural plot that are notidentifiable as fruit) are discarded.

In some embodiments, the method further comprises determining from theplurality of images, for each fruit of the plurality of fruit, arespective fruit color. In some embodiments, determining fruit colorcomprises i) identifying from the plurality of images, for each fruit ofthe plurality of fruit, a corresponding contour, ii) extracting, fromeach respective contour one or more color feature descriptors, and iii)determining a respective color for each fruit of the plurality of fruit.In some embodiments, the method further comprises assigning a respectivematurity stage to each fruit of the plurality of fruit based at least inpart on the respective fruit color.

IV. A Computational Model for Identifying Fruit from Images.

Another aspect of the present disclosure provides a method of training acomputational model to identify fruit in agricultural plot images. Themethod proceeds by obtaining, in electronic format, a training dataset,wherein the training dataset comprises a plurality of training imagesfrom a plurality of agricultural plots, wherein each training image isfrom a respective agricultural plot in the plurality of agriculturalplots and comprises at least one identified fruit. The method furtherincludes determining, for each respective fruit in each respectivetraining image in the plurality of training images, a correspondingcontour, and a corresponding fruit size. The method further comprisestraining an untrained or partially trained computational model using atleast the corresponding contour and corresponding fruit size for eachrespective fruit in each respective training image in the plurality oftraining images, thereby obtaining a first trained computational modelthat is configured to identify fruit in agricultural plot images.

Other embodiments are directed to systems, portable consumer devices,and computer readable media associated with the methods describedherein. As disclosed herein, any embodiment disclosed herein can beapplied in some embodiments to any other aspect. Additional aspects andadvantages of the present disclosure will become readily apparent tothose skilled in this art from the following detailed description, whereonly illustrative embodiments of the present disclosure are shown anddescribed. As will be realized, the present disclosure is capable ofother and different embodiments, and its several details are capable ofmodifications in various obvious respects, all without departing fromthe disclosure. Accordingly, the drawings and description are to beregarded as illustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The implementations disclosed herein are illustrated by way of example,and not by way of limitation, in the figures of the accompanyingdrawings. Like reference numerals refer to corresponding partsthroughout the several views of the drawings.

FIG. 1 illustrates example image collection routes in an agriculturalplot, in accordance with some embodiments of the present disclosure.

FIGS. 2A and 2B collectively illustrate examples of different treecanopy views, in accordance with some embodiments of the presentdisclosure.

FIGS. 3A and 3B illustrate examples of fruit identification (e.g.,through bounding boxes or contours) in images, in accordance with someembodiments of the present disclosure.

FIG. 4 illustrates an example histogram of fruit sizes that is providedin accordance with some embodiments of the present disclosure.

FIG. 5 illustrates example communications connections of variousdevices, in accordance with some embodiments of the present disclosure.

FIG. 6 illustrates a block diagram of a system (e.g., an exemplaryserver) in accordance with some embodiments of the present disclosure.

FIG. 7 illustrates a block diagram of a system (e.g., an exemplaryvehicle or other image collection device) in accordance with someembodiments of the present disclosure.

FIGS. 8A, 8B, and 8C collectively illustrate an example of object sizedetermination based on multiple viewing angles, performed in accordancewith some embodiments of the present disclosure.

FIG. 9 illustrates an example user interface in accordance with someembodiments of the present disclosure.

FIG. 10 illustrates an example user interface in accordance with someembodiments of the present disclosure.

FIG. 11 illustrates an example user interface in accordance with someembodiments of the present disclosure.

FIGS. 12A and 12B collectively illustrate a block diagram of methodsteps described herein, in accordance with some embodiments of thepresent disclosure, where boxes with dashed outlines represent optionalsteps.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

The implementations described herein provide various technical solutionsfor predicting the yield of an agricultural plot.

Several aspects are described below with reference to exampleapplications for illustration. It should be understood that numerousspecific details, relationships, and methods are set forth to provide afull understanding of the features described herein. One having ordinaryskill in the relevant art, however, will readily recognize that thefeatures described herein can be practiced without one or more of thespecific details or with other methods. The features described hereinare not limited by the illustrated ordering of acts or events, as someacts can occur in different orders and/or concurrently with other actsor events. Furthermore, not all illustrated acts or events are requiredto implement a methodology in accordance with the features describedherein.

Note that, while the remainder of this document refers often to fruitsize and yield, one of skill in the art having the benefit of thisdisclosure will understand that many of the systems and methodsdescribed herein are applicable to any crop, including non-fruit crops(lettuce, turnips, broccoli, etc.) Further, the term “fruit” should beconstrued to include the fruiting body of any plant. For example,walnuts, peppers, beans, and the like, are all fruit, as are oranges,apples, etc.

Further, although some embodiments of the present disclosure refer toimages obtained aerially (e.g., from a unmanned aerial vehicle or asatellite), in some embodiments, ground-based imagery can be used tosupplement or replace the aerial imagery. One of skill in the art havingthe benefit of this disclosure will understand to which embodimentsground-based imagery may be applicable.

FIG. 1 illustrates example image collection routes 110 (e.g., route110-a and route 110-b) in an agricultural plot 100, in accordance withsome embodiments of the present disclosure. In some embodiments, theimage collection routes 110 are traversed by a drone 202 or othervehicle situated (e.g., flying) at a first height (e.g., 3-10 m) abovethe orchard to obtain images at a first resolution (e.g., which aremeasured in pixels per millimeter) during a scouting mission (alsoreferred to as a drone scouting mission or a drone mission). The imagescollected during image collection routes 110 are used to estimate fruityield for the agricultural plot, as described throughout the presentdisclosure.

In some embodiments, the agricultural plot comprises a plurality oftrees 102 arranged in rows (e.g., the agricultural plot is an orchard).Route 110-a is an example in which drone 202 or another UAV flies overan entire row in the orchard, switches to the next row, and flies overthat entire row. However, route 110-b illustrates other embodiments orcircumstances in which the route takes the UAV between rows (e.g.,without reaching the end of the row). Thus, unlike terrestrial-basedvehicles (e.g., tractors), UAVs are able to scout a subset (e.g., lessthan all) of the trees in a plurality of different rows. Using thesystems and methods described herein, accurate fruit yield estimates canbe determined using more efficient (e.g., shorter) routes, as comparedto conventional methods of estimating fruit yield.

As described in greater detail below, in some embodiments, the drone 202or other vehicle obtains two or more images from a second height (e.g.,a height greater than the first height). Note that, in some embodiments,a first vehicle (e.g., drone 202) obtains the images from the firstheight and a second vehicle (e.g., satellite 506, FIG. 5 ) differentfrom the first vehicle obtains the images from the second height. Insome embodiments, the second height provides a bird's eye view of theentire agricultural plot, or a birds' eye view of at least a region ofthe agricultural plot. From the second height, various spatialdistributions of characteristics (e.g., growing conditions, tree heath)affecting fruit yield (e.g., slope, aspect, moisture level) are observedand determined. The spatial distribution of characteristics is used, insome embodiments, to inform the selection of image collection routes 110and to scale the resulting fruit yield estimates to more accuratelyreflect the entire agricultural plot. For example, route 110-b isselected to visit a plurality of different regions of the agriculturalplot with a plurality of different characteristics and/or growingconditions, so as to more accurately sample fruit growth without needingto visit every tree in the orchard.

FIGS. 2A and 2B collectively illustrate examples of different treecanopy views, in accordance with some embodiments of the presentdisclosure. FIG. 2A illustrates that, in some embodiments, imagesobtained from above the agricultural plot are obtained at or below thetop of the trees 102 (e.g., the images are obtained from a top-side viewof the trees 102). FIG. 2B, on the other hand, illustrates that, in someembodiments, images obtained from above the agricultural plot areobtained above the top of the trees 102 (e.g., the images are of a topof the canopy of the trees 102). The images include fruit 206 (e.g.,fruit 206-a in FIG. 2A and fruit 206-b in FIG. 2B).

As described in greater detail throughout this disclosure, in someembodiments, individual fruit 206 are identified and tracked throughoutdifferent images (e.g., assigned a unique fruit identified). Forexample, the same fruit 206-a is identified in two different imagesobtained by the same respective vehicle (e.g., identified in imagesacquired by the same camera on the same vehicle) when the respectivevehicle is at two different positions in the orchard (e.g., the fruit ismatched between images). In some embodiments, using two images thatinclude the same fruit, as well as knowledge of the locations at whichthe two images were taken, a size of the fruit is determined based on adisparity of the size of the fruit in the two images (e.g., thedisparity in the imaged size of the fruit is used to estimate the sizeof the fruit through triangulation, as described with reference to FIG.8 ). In some embodiments, the two images that include the same fruitwere collected at the same height (e.g., the two images are from eitherthe first height or the second height). In some embodiments, theestimate of the size of individual fruit is performed in this manner fora plurality of fruit 206 in the orchard, and the respective fruit sizesare used to estimate the overall yield for the orchard.

Note that, regardless of where the images are taken from (e.g., the topside view or the top canopy view), only a fraction (less than all) ofthe fruit will be visible to the camera, and thus detectable. Thus, someembodiments scale fruit yield estimations based on a scaling factor todetermine an estimate of fruit yield that includes fruit not visible(not detectable) in the images obtained by the drone 202. In someembodiments, scaling of (e.g., extrapolating) fruit yield estimations isnon-linear (e.g., more than one scaling factor or a scaling factorequation is used).

FIGS. 3A and 3B illustrate examples of fruit identification (e.g.,through bounding boxes or contours) in images, in accordance with someembodiments of the present disclosure. FIG. 3A illustrates an example inwhich bounding boxes 302 are identified for each fruit 206. FIG. 3Billustrates an example in which curved contours 310 are identified(representing the margins of each fruit 206). The curved contours 310provide information about the shape of each fruit, which, in someembodiments, is provided to the user (e.g., through a graphical userinterface). In either case, with appropriate scaling, disparities in thecontour for an identified fruit between two or more images, togetherwith knowledge of where the two or more images where taken (e.g., from aroute record) can be used to determine a size of the identified fruit.

FIG. 4 illustrates an example histogram of fruit sizes that is providedin accordance with some embodiments of the present disclosure. Thehistogram of fruit sizes provides a count or percentage of fruit foreach respective fruit size bin. In some embodiments, the histogram offruit sizes provides a projected (estimated) distribution of fruit sizesfor a later time (e.g., at the time of harvest) based at least in parton one or more sets of current or past observations (e.g., where thesets of observations include the images described throughout thisdisclosure). In some embodiments, the method further comprises providinga histogram of fruit color. In some embodiments, the histogram of fruitcolors provides a count or percentage of fruit for each respective fruitcolor bin. In some embodiments, the histogram of fruit colors provides aprojected distribution of fruit colors for a later time (e.g., for thetime of harvest) based at least in part on one or more sets of currentor past observations. In some embodiments, the method further comprisesproviding a histogram of fruit maturity. In some embodiments, thehistogram of fruit maturity provides a count or percentage of fruit foreach respective maturity stage (e.g., ripe vs unripe). In someembodiments, the histogram of fruit maturity provides a projecteddistribution of fruit maturity stages for a later time (e.g., for thetime of harvest) based at least in part on one or more sets of currentor past observations. In some embodiments, binning for the histogram offruit color and/or the histogram of fruit maturity is based at least inpart on the respective fruit type being considered.

FIG. 5 illustrates example communications connections of variousdevices, in accordance with some embodiments of the present disclosure.For example, in some embodiments, drone 202, server 502, satellite 506,and user interface 508 (e.g., a device with a human-operable userinterface, such as a desktop computer, laptop computer, tablet, mobilephone, or the like) communicate through one or more communicationsnetworks 504. In some embodiments, the one or more networks 504 includepublic communication networks, private communication networks, or acombination of both public and private communication networks. Forexample, the one or more networks 504 can be any network (or combinationof networks) such as the Internet, other wide area networks (WAN), localarea networks (LAN), virtual private networks (VPN), metropolitan areanetworks (MAN), peer-to-peer networks, and/or ad-hoc connections. Insome embodiments, drone 202 and/or server 502 communicate with satellite506 using TCP/IP.

Details of exemplary systems are now described in conjunction with FIGS.6 and 7 . FIG. 6 illustrates an exemplary server 502 as shown in FIG. 5. In some embodiments, server 502 includes one or more processing unitsCPU(s) 602 (also referred to as processors), one or more networkinterfaces 604, memory 611 for storing programs and instructions forexecution by the one or more processors 602, one or more communicationsinterfaces such as input/output interface 606, and one or morecommunications buses 610 for interconnecting these components.

The one or more communication buses 610 optionally include circuitry(sometimes called a chipset) that interconnects and controlscommunications between system components. The memory 611 typicallyincludes high-speed random access memory, such as DRAM, SRAM, DDR RAM,ROM, EEPROM, flash memory, CD-ROM, digital versatile disks (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. Memory 611 optionally includesone or more storage devices remotely located from the CPU(s) 602. Memory611, and the non-volatile memory device(s) within the memory 611,comprise a non-transitory computer readable storage medium.

In some embodiments, memory 611 or alternatively the non-transitorycomputer readable storage medium stores the following programs, modulesand data structures, or a subset thereof:

-   -   Operating system 616, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   Network communication module (or instructions) 618 for        connecting server 502 with other devices, or a communication        network;    -   Drone scouting mission database 620, which includes:        -   A plurality of drone missions 622, each comprising, for each            image 624 in a plurality of images, a respective contour 628            for one or more fruit 626;    -   Contour validation module 630 that includes instructions for        validating identified fruit contours 628, where fruit contours        that do not meet validation criteria are removed;    -   Fruit identification module 640 that includes instructions for        detecting fruit 626 from images 624;    -   Fruit sizing module 650 that includes instructions for        determining respective fruit size, for identified and validated        fruit contours 628; and    -   Optionally, other modules 660 that include instructions for        handling other functions and aspects described herein.

In some embodiments, optional modules 660 include a color detectionmodule that includes instruction for determining respective fruit color,for identified and validated fruit contours 628. In some embodiments,the plurality of drone missions 622 further comprise, for each image 624a respective color for one or more fruit.

In various implementations, one or more of the above identified elementsare stored in one or more of the previously mentioned memory devices,and correspond to a set of instructions for performing a functiondescribed above. The above identified modules, data, or programs (e.g.,sets of instructions) need not be implemented as separate softwareprograms, procedures, datasets, or modules, and thus various subsets ofthese modules and data may be combined or otherwise re-arranged invarious implementations. In some implementations, memory 611 optionallystores a subset of the modules and data structures identified above.Furthermore, in some embodiments, the memory stores additional modulesand data structures not described above. In some embodiments, one ormore of the above identified elements is stored in a computer system,other than that of server 502, that is addressable by server 502 so thatserver 502 may retrieve all or a portion of such data when needed.

Although FIG. 6 depicts server 502, the figure is intended more as afunctional description of the various features that may be present incomputer systems than as a structural schematic of the implementationsdescribed herein. In practice, and as recognized by those of ordinaryskill in the art, items shown separately could be combined and someitems could be separated.

FIG. 7 illustrates an exemplary drone 202 as shown in FIG. 5 . In someembodiments, drone 202 includes one or more processing units CPU(s) 702(also referred to as processors), one or more network interfaces 704,memory 711 for storing programs and instructions for execution by theone or more processors 702, one or more communications interfaces suchas input/output interface 706, and one or more communications buses 710for interconnecting these components.

The one or more communication buses 710 optionally include circuitry(sometimes called a chipset) that interconnects and controlscommunications between system components. The memory 711 typicallyincludes high-speed random access memory, such as DRAM, SRAM, DDR RAM,ROM, EEPROM, or flash memory, CD-ROM, digital versatile disks (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. The database 712 optionallyincludes one or more storage devices remotely located from the CPU(s)702. The database 712, and the non-volatile memory device(s) within thedatabase 712, comprise a non-transitory computer readable storagemedium. In some implementations, memory 711 and/or the non-volatilememory device(s) within memory 711 comprises a non-transitory computerreadable storage medium.

In some embodiments, memory 711 and/or the non-transitory computerreadable storage medium comprising memory 711 stores the followingprograms, modules and data structures, or a subset thereof, sometimes inconjunction with the database 712:

-   -   Operating system 716, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   Network communication module (or instructions) 718 for        connecting drone 202 with other devices, or a communication        network;    -   Drone scouting mission database 720, which includes:        -   A plurality of drone missions 722, each comprising, for each            image 724 in a plurality of images, a respective contour 728            for one or more fruit 726;    -   Drone route database 730, which includes:        -   A plurality of route records 732, each comprising a            respective route 734 through an agricultural plot and a            respective drone velocity 736;    -   One or more flight control modules 740 that includes        instructions for piloting; and    -   Optionally, other modules 750 that include instructions for        handling other functions and aspects described herein.

In various implementations, one or more of the above identified elementsare stored in one or more of the previously mentioned memory devices,and correspond to a set of instructions for performing a functiondescribed above. The above identified modules, data, or programs (e.g.,sets of instructions) need not be implemented as separate softwareprograms, procedures, datasets, or modules, and thus various subsets ofthese modules and data may be combined or otherwise re-arranged invarious implementations. In some implementations, the memory 711optionally stores a subset of the modules and data structures identifiedabove. Furthermore, in some embodiments, the memory stores additionalmodules and data structures not described above. In some embodiments,one or more of the above identified elements is stored in a computersystem, other than that of drone 202, that is addressable by drone 202so that drone 202 may retrieve all or a portion of such data whenneeded.

Although FIG. 7 depicts drone 202, the figure is intended more as afunctional description of the various features that may be present incomputer systems than as a structural schematic of the implementationsdescribed herein. In practice, and as recognized by those of ordinaryskill in the art, items shown separately could be combined and someitems could be separated.

In some embodiments, the one or more flight modules 750 controlpositioning of a respective vehicle (e.g., either a manned or unmannedaircraft) within a predefined distance threshold (e.g., the location ofdrone 202 at every time is controlled within a predefined distancethreshold). In some embodiments, the predefined distance threshold isless than 1 foot, less than 2 feet, less than 3 feet, less than 4 feet,less than 5 feet, less than 6 feet, less than 7 feet, less than 8 feet,less than 9 feet, less than 10 feet, less than 20 feet, less than 30feet, less than 40 feet, or less than 50 feet. In some embodiments, adifferent predefined distance threshold is used for each respectivevehicle (e.g., there is a distinct predefined distance threshold for adrone vs. a satellite).

Stereo Triangulation and Determining Fruit Size

In some embodiments, the methods herein provide for identifying fruitsize based on analysis of two or more images (e.g., two or more RGBimages). In some embodiments, the analysis of the two or more imagescomprises determining a respective image resolution (e.g., a conversionof size to pixels) for each image in the two or more images. In someembodiments, a respective initial fruit size is determined for aparticular fruit in each image in the two or more images. In some suchembodiments, a respective size for the particular fruit is determinedbased on the disparity in initial fruit sizes from the two or moreimages.

Images used for fruit sizing are selected based on contour analysis. Forexample, in some embodiments, the two or more images each include acontour corresponding to a same fruit (e.g., a fruit that has beenassigned a respective fruit identifier). In some embodiments, the two ormore images each include a contour corresponding to a same tree. In someembodiments, the two or more images are adjacent images (e.g., adjacentin time). In some embodiments, the two or more images are from differenttime points. In some embodiments, the two or more images are fromdifferent pluralities or subsets of images (e.g., one image is from thefirst subset of images and the second image is from the second subset ofimages). For example, two images taken from different heights in theagricultural plot may include a same fruit (e.g., contours identified asbelonging to an individual fruit).

In some embodiments, the two or more images are matched based on contouranalysis (e.g., based on determining that each image in the two or moreimages includes a respective contour corresponding to a same fruit). Insome embodiments, the two or more images are not matched or analyzed byusing a composite image (e.g., there is no matching of pixels betweenthe two or more images in order to determine fruit size).

FIGS. 8A, 8B, and 8C collectively illustrate an example of how stereotriangulation can be used to determine the size of a respective fruit(e.g., by using at least two images). As shown in FIG. 8A, in someembodiments, there are at least two camera locations 804A and 804B. Eachcamera location corresponds to the location (e.g., as determined by GPS,IMU, Kalman filtering, etc., or a combination thereof) of at least oneimage (e.g., in a respective vehicle route through an agriculturalplot), where each image comprises a respective object (e.g., a fruit).In some embodiments, these two locations are two separate vehiclelocations (e.g., separate camera locations as determined either by thesame vehicle or by two different vehicles). In some embodiments, a samecamera obtains the at least two images from the two camera locations8014. In some embodiments, different cameras are used to obtain one ormore of the images. In some embodiments, these two locations are merelytwo separate cameras (e.g., on the same UAV or other vehicle).

In some embodiments, each location has its own respective coordinatereference system, centered on the lens that obtained the image (e.g.,represented as 804A and 804B, respectively, in FIG. 8A).

With regards to FIGS. 8A, 8B, and 8C and the equations for triangulatingan object (e.g., a fruit) from two or more images in the plurality ofimages, the following variables are defined. In some embodiments, FIGS.8B and 8C illustrate the focal lengths 822A and 822B of the camera(s) atlocations 804A and 804B, respectively.

In some embodiments, Δx corresponds to the estimated distance traveledby the camera (e.g., either the distance traveled by a first camera on afirst vehicle, or the distance between a first camera on a first vehicleand a second camera on a second vehicle) between images. In someembodiments, Δx requires knowing the respective velocity of therespective vehicle, and Δx is estimated by integrating the velocity overthe time period between the two images (the time between when each imagewas obtained). In some embodiments, Δx is determined as the distancebetween the two camera locations 804A and 804B (e.g., either thesummation of 810A and 808B or the summation of 808A and 810B).

In some embodiments, X₁ ^(L) corresponds to the physical distance 808Aof the left edge 816A of object 802 from camera location 804A.Similarly, X₁ ^(R) corresponds to the physical distance 808B from theleft edge 816A of object 802 from camera location 804B.

In some embodiments, X₂ ^(L) corresponds to the physical distance 810Aof the right edge 816B of object 802 from camera location 804A.Similarly, X₂ ^(R) corresponds to the physical distance 808B from theright edge 816B of object 802 from camera location 804B.

In some embodiments, d₁ and d₂ respectively correspond to the verticaldistances (806A and 806B) of the left and right camera locations (804Aand 804B) from a plane on which the object edge is located.

In some embodiments, f corresponds to the focal length (822A and 822B)of the camera(s), which is determined from the specification(s) of thecamera(s) used for obtaining the images. In some embodiments, a samecamera with a same focal point is used to obtain the at least twoimages. In some embodiments, a different respective camera (with eithera same or different focal point) is used to obtain each of the images inthe at least two images.

In some embodiments, x^(L) corresponds to the pixel coordinates of theobject in the left camera image (e.g., 820A), and x^(R) corresponds tothe pixel coordinates of object 802 in the right camera image (820B). Insome embodiments, x^(L) and x^(R) are each scaled by the camera sensorsize, as reported by the camera specifications. In some embodiments, theleft and right cameras (e.g., at positions 804A and 804B) define theirown coordinate reference systems.

In some embodiments, when the camera is at full resolution, the camerasensor size refers to the physical size of the light sensor attributedto each individual pixel. In some embodiments, when the camera is atvariable resolution, the camera sensor size is estimated by dividing thesize of the full sensor by the pixel dimensions of the at least twoimages.

In some embodiments, the width of object 802 can be derived fromgeometric and algebraic principles. In particular, the rule of similartriangles can be used to obtain the relations:

$\begin{matrix}{{\frac{f}{x_{1}^{L}} = {- \frac{d_{1}}{X_{1}^{L}}}},{\frac{f}{x_{1}^{R}} = {- \frac{d_{2}}{X_{1}^{R}}}}} & (1)\end{matrix}$

In some embodiments,

$\begin{matrix}{{\Delta d} = {{d_{1} - d_{2}} = {{{- \frac{{fX}_{1}^{L}}{x_{1}^{L}}} + \frac{{fX}_{1}^{R}}{x_{1}^{R}}} = {f\left( {{- \frac{X_{1}^{L}}{x_{1}^{L}}} + \frac{X_{1}^{R}}{x_{1}^{R}}} \right)}}}} & (2)\end{matrix}$

In some embodiments, equation 2 can be further simplified, using X₁^(R)=X₁ ^(L)−Δx as follows:

$\begin{matrix}{{\Delta d} = {f\left( {{- \frac{X_{1}^{L}}{x_{1}^{L}}} + \frac{X_{1}^{L} - {\Delta x}}{x_{1}^{R}}} \right)}} & (3)\end{matrix}$ $\begin{matrix}{{\frac{\Delta d}{f} + \frac{\Delta x}{x_{1}^{R}}} = {X_{1}^{L}\left( {\frac{1}{x_{1}^{R}} - \frac{1}{x_{1}^{L}}} \right)}} & (4)\end{matrix}$ $\begin{matrix}{X_{1}^{L} = {\left( {\frac{\Delta d}{f} + \frac{\Delta x}{x_{1}^{R}}} \right)\frac{x_{1}^{R}x_{1}^{L}}{x_{1}^{L} - x_{1}^{R}}}} & (5)\end{matrix}$

In some embodiments, the physical width (e.g., ΔX) of object 802 (e.g.,the diameter of a fruit) can then be calculated as:

$\begin{matrix}{{\Delta X} = {{X_{2}^{L} - X_{1}^{L}} = {{X_{2}^{R} - X_{1}^{R}} = {{\frac{\Delta d}{f}\left\lbrack {\frac{x_{2}^{R}x_{2}^{L}}{x_{2}^{L} - x_{2}^{R}} - \frac{x_{1}^{R}x_{1}^{L}}{x_{1}^{L} - x_{1}^{R}}} \right\rbrack} + {\Delta{x\left\lbrack {\frac{x_{2}^{L}}{x_{2}^{L} - x_{2}^{R}} - \frac{x_{1}^{L}}{x_{1}^{L} - x_{1}^{R}}} \right\rbrack}}}}}} & (6)\end{matrix}$

Using equation 6 it is possible, in some embodiments, to determine thesize of any object 802, including, but not limited to, fruit and trees.In some embodiments, the physical width of object 802 is determinedbased at least in part on the resolution of the respective images (e.g.,images obtained at the two camera locations 804A and 804B).

FIG. 9-11 illustrate example user interfaces (e.g., graphical userinterfaces) in accordance with some embodiments of the presentdisclosure.

The graphical user interface shown in FIG. 9 displays a birds' eye viewof an agricultural plot (e.g., agricultural plot 100, FIG. 1 ). From thebirds' eye view, it is apparent that different regions of theagricultural plot have different characteristics (growing conditions)that may affect yield. In some embodiments, the graphical user interfaceshown in FIG. 9 allows users to build new routes and visualize existingroutes, so that representative paths through the agricultural plot aretaken (flown over). Information obtained from the representative pathsare, in some embodiments, combined with plot-wide information (e.g.,information about the different growing conditions in different regionsof the agricultural plot) to provide a more accurate estimate of thefruit yield for the entire plot.

FIG. 10 illustrates a graphical user interface in which a number oftrees and the canopy areas of those individual trees are identified. Insome embodiments, the fruit yield estimates described herein are scaledby the number and canopy areas of the trees in the agricultural plot.

FIG. 11 illustrates a graphical user interface in which differentcharacteristics of individual trees (e.g., NDVI—normalized differencevegetation index, canopy area, tree height, and tree volume) can bevisualized. For example, through the graphical user interface shown inFIG. 11 , a user can toggle between these different characteristics ofthe individual trees. A size (e.g., radius) of the graphicalrepresentations of the individual trees is based on a value for theselected characteristic.

While systems and graphical user interfaces in accordance with thepresent disclosure have been disclosed above, methods in accordance withthe present disclosure are now detailed with regards to FIGS. 12A and12B.

Embodiments Directed Towards Predicting Fruit Size and/or Fruit Yield.

Multiple aspects of the present disclosure are provided for predictingyield of fruit for an agricultural plot.

In some embodiments, the fruit for which the yield of fruit is predictedare of a fruit type selected from the group consisting of blueberries,cherries, plums, peaches, nectarines, apricots, olives, mangos, pears,apples, quinces, loquats, citrus, figs, papayas, avocados, coconuts,durians, guavas, persimmons, and pomegranates. In some embodiments, thesystems and methods described herein are equally applicable to othercrops, such as non-fruit crops (e.g., corn, beans, brassicas, etc.).

Block 1202 of FIG. 12A.

The method(s) proceed 1202 by obtaining at least a first plurality ofimages of an agricultural plot. In some embodiments, the method furthercomprises using a first vehicle (e.g., such as drone 202) to obtain thefirst plurality of images. In some embodiments, the method furthercomprises using a second vehicle (e.g., drone 202 or another vehicle) toobtain a second plurality of images. In some embodiments, the firstvehicle is the same vehicle as the second vehicle. In some embodiments,the first vehicle is distinct from the second vehicle. In someembodiments, the first and/or second vehicle comprises a satellite. Insome embodiments, the first and/or second vehicle comprises a drone. Insome embodiments, the first vehicle is a satellite, and the secondvehicle is a drone.

In some embodiments, the first and/or second vehicle (e.g., a droneand/or a satellite etc.) includes an RGB camera. In some embodiments,the first and/or second vehicle includes an RGB camera and each image inat least the first plurality of images comprises an RGB image. In someembodiments, each image in the second plurality of images comprises anRGB image.

One aspect of the present disclosure provides a method of predicting ayield of fruit growing in an agricultural plot using images from two ormore time points. The method comprises obtaining, at a first time, afirst plurality of images of a canopy of the agricultural plot from anaerial view of the canopy of the agricultural plot. The method alsocomprises obtaining, at a second time, a second plurality of images ofthe canopy of the agricultural plot from the aerial view of the canopyof the agricultural plot.

Another aspect of the present disclosure provides a method of predictinga yield of fruit growing in an agricultural plot from a plurality offruit sizes, using two or more images to identify individual fruit andfruit sizes. The method comprises obtaining, using a camera, a firstplurality of images of a canopy of the agricultural plot. The methodfurther includes obtaining, using the camera, a second plurality ofimages of the canopy of the agricultural plot.

In some embodiments, the method further comprises obtaining a firstroute record for the first vehicle corresponding to the first pluralityof images. In some embodiments, the method further comprises obtaining asecond route record for the second vehicle corresponding to the secondplurality of images.

Yet another aspect of the present disclosure provides a method ofpredicting a yield of fruit growing in an agricultural plot using imagesfrom multiple heights. The method comprises obtaining a first pluralityof images of a canopy of the agricultural plot from an aerial view ofthe canopy of the agricultural plot. The first plurality of imagesincludes a first subset of images and a second subset of images. Themethod further includes obtaining a first route record for the firstvehicle corresponding to the first subset of images, and a second routerecord for the second vehicle corresponding to the second subset ofimages. Note that in some embodiments in which the first subset ofimages and the second subset of images were acquired on the same flight,the first route record and the second route record may be portions of asingle route record. In some such embodiments, the first vehicle and thesecond vehicle are a same vehicle.

In some embodiments, the first and the second times are separated by atleast 5 minutes, at least 10 minutes, or at least 30 minutes. In someembodiments, the first and the second times are separated by at least 1hour, at least 2 hours, at least 4 hours, at least 6 hours, at least 8hours, at least 10 hours, or at least 12 hours. In some embodiments, thefirst and the second times are separated by at least 1 day, at least 2days, at least 3 days, at least 4 days, at least 5 days, or at least 6days. In some embodiments, the first and the second times are separatedby at least 1 week, at least 2 weeks, or at least 3 weeks. In someembodiments, the two or more time points are separated by at least 1month, at least 2 months, at least 3 months, at least 4 months, at least5 months, at least 6 months, at least 7 months, at least 8 months, atleast 9 months, at least 10 months, at least 11 months, or at least 12months.

In some embodiments, the first route record comprises i) a first routeover the agricultural plot, and ii) a respective velocity of the firstvehicle for each image in the first subset of images. In someembodiments, the second route record comprises i) a second route overthe agricultural plot, and ii) a respective velocity of the secondvehicle for each image in the second subset of images. In someembodiments, as described below, the first route record furthercomprises one or more additional features for each respective image inthe first subset of images. Likewise, in some embodiments, the secondroute record further comprises one or more additional features for eachrespective image in the second subset of images.

In some embodiments, the first route record further comprises arespective height and a respective location (e.g., as determined by aGNSS—a Global Navigation Satellite System such as GPS, GLONASS, Galileo,Beidou (generally referred to as GPS throughout), IMU—inertialmeasurement unit, Kalman filtering, etc., or a combination thereof) foreach image in the first subset of images, and each image in the firstsubset of images is evaluated for satisfaction of one or more validationcriteria. In some embodiments, one or more images in the first subset ofimages that fail the one or more validation criteria are discarded. Insome embodiments, the second route record further comprises a respectiveheight and a respective location (e.g., detected by GPS, IMU—inertialmeasurement unit, Kalman filtering, etc., or a combination thereof) foreach image in the second subset of images, and each image in the secondsubset of images is evaluated for satisfaction of one or more validationcriteria. In some embodiments, one or more images in the second subsetof images that fail the one or more validation criteria are discarded.In some embodiments, the one or more validation criteria comprisedetermining whether each image includes a respective fruit contour. Insome embodiments, the one or more validation criteria comprisedetermining whether each image is in focus. In some embodiments, thefirst route record further comprises iii) a respective timestamp foreach image in the first plurality of images. In some embodiments, thesecond route record further comprises iii) a respective timestamp in thesecond plurality of images.

Referring to block 1204, in some embodiments, each image in the firstplurality of images is of a canopy of the agricultural plot. In someembodiments, the first plurality of images of the canopy of theagricultural plot comprises images of a plurality of plants growing inthe agricultural plot. In some embodiments, the second plurality ofimages of the canopy of the agricultural plot comprises images of aplurality of plants growing in the agricultural plot.

Referring to block 1206, in some embodiments, the first plurality ofimages includes a first subset of images and a second subset of images.In some such embodiments, each image in the first subset of imagescorresponds to a respective first portion of the agricultural plot, andeach image in the second subset of images corresponds to a respectivesecond portion of the agricultural plot. In some embodiments, the secondportion of the agricultural plot is smaller than the first portion ofthe agricultural plot (e.g., the first portion corresponds to the entireagricultural plot or a larger subset of the agricultural plot, while thesecond portion corresponds to a smaller subset of the agriculturalplot). In some embodiments, each image in the first subset of images hasa corresponding first resolution, and each image in the second subset ofimages has a corresponding second resolution, where the secondresolution is higher than the first resolution.

In other words, in some embodiments, each image in the first subset ofimages corresponds to a respective lower-resolution image of a largerportion of the agricultural plot (e.g., a satellite image), while eachimage in the second subset of images corresponds to a respectivehigher-resolution images of a smaller portion of the agricultural plot(e.g., a close-up image of the canopy of the agricultural plot obtainedby a drone).

In some embodiments, the first subset of images is obtained at a firstheight and the second subset of images is obtained from a second height.In some embodiments, the second height is different from the firstheight. In some embodiments, the second plurality of images includes athird subset of images, and a fourth subset of images. In someembodiments, each image in the third subset of images corresponds to arespective first portion of the agricultural plot, and each image in thefourth subset of images corresponds to a respective second portion ofthe agricultural plot. In some such embodiments, the second portion ofthe agricultural plot is smaller than the first portion of theagricultural plot. In some embodiments, each image in the third subsetof images has a corresponding first resolution, and each image in thefourth subset of images has a corresponding second resolution, whereinthe second resolution is higher than the first resolution. In someembodiments, the third subset of images is obtained at a first heightand the fourth subset of images is obtained from a second height.

In some embodiments, each image in the first and second subsets ofimages comprises a respective RGB image. In some embodiments, each imagein the third and fourth subsets of images comprises a respective RGBimage.

In some such embodiments, the first height is at least 80 m above thecanopy (e.g., above the agricultural plot). In some such embodiments,the second height is at least 3 m above the canopy (e.g., above theagricultural plot).

In some embodiments, the first height is at least 20 m, at least 30 m,at least 40 m, at least 50 m, at least 60 m, at least 70 m, at least 80m, at least 90 m, or at least 100 m, above the canopy of one or moreplants growing in the agricultural plot. In some embodiments, the firstheight is measured from a predefined reference height (e.g., groundlevel). For example, in some circumstances, the first height is 80 mabove ground level. In some embodiments, images taken at the firstheight permit determination of plant sizes, characteristics of theentire agricultural plot, a number of fruit growing in the agriculturalplot, and/or characteristics of multiple agricultural plots.

In some embodiments, the second height is at least 1 m, at least 2 m, atleast 4 m, at least 5 m, at least 6 m, at least 7 m, at least 8 m, atleast 9 m, or at least 10 m above the canopy of the one or more plantsgrowing in the agricultural plot (or, alternatively, above thepredefined reference height). In some embodiments, the second height ismeasured from ground level (e.g., 3 m above ground level). In someembodiments, the second height is at or below canopy level (e.g., may beat a height under the canopy level of one or more plants growing in theagricultural plot).

Referring to block 1208, in some embodiments, the first plurality ofimages comprises at least two respective images for each camera locationin a plurality of camera locations in the agricultural plot.

Referring to block 1210, in some embodiments, the method(s) furthercomprise obtaining a second plurality of images of the agriculturalplot. In some embodiments, with regards to block 1212, the secondplurality of images is from a second time. In some embodiments, withregards to block 1214, the second plurality of images is from a secondtime and a second camera location.

Block 1216 of FIGS. 12A and 12B.

The method(s) proceed 1216 by processing at least the first plurality ofimages and identifying one or more fruit.

Referring to block 1218, in another aspect of the present disclosure,the method further includes, for each respective fruit of a plurality offruit growing in the agricultural plot, identifying 1218 the respectivefruit in a first respective image in the first plurality of images,identifying the respective fruit in a second respective image in thefirst plurality of images, and using at least i) the first and secondrespective images and ii) a distance between the first and second cameralocations to determine a corresponding size of the respective fruit. Thefirst respective image has a corresponding first camera location, andthe second respective image has a corresponding second camera location.In some embodiments, the distance between the first and second cameralocations is determined using a route record. In some embodiments, thedistance between the first and second camera locations is a distancetraveled by a respective vehicle housing the respective camera (e.g.,drone 202). In some embodiments, using a route record allows fruit sizesto be determined based on a disparity of the apparent fruit sizes in thetwo images, without the need for additional cameras onboard a respectivevehicle (e.g., without the need for additional cameras to providestereoscopic views of the fruit, since the stereoscopic views areobtained by moving the respective vehicle).

In some embodiments, the method further comprises using a depth of focusof the camera (e.g., an RGB camera on a UAV or other vehicle) todetermine a corresponding size of each respective fruit. In someembodiments, a respective fruit size is determined via triangulation asdescribed above. In some embodiments, the camera comprises a stereocamera.

Referring to block 1220, in one aspect of the present disclosure, themethod further includes estimating, from the first plurality of imagesof the canopy of the agricultural plot obtained from the aerial view ofthe canopy of the agricultural plot, a first number of fruit detectablefrom the first plurality of images of the canopy of the agriculturalplot. The method further includes estimating from the second pluralityof images of the canopy of the agricultural plot obtained from theaerial view of the canopy of the agricultural plot, a second number offruit detectable from the second plurality of images of the canopy ofthe agricultural plot.

Referring to block 1222, in yet another aspect of the presentdisclosure, the method further comprises estimating, from the firstsubset of images of the canopy of the agricultural plot obtained fromthe aerial view of the canopy of the agricultural plot, a number offruit detectable from the plurality of images of the canopy of theagricultural plot. The method further includes determining, from thesecond subset of images, for each fruit of a plurality of fruit, arespective fruit size.

In some embodiments, the method further comprises estimating, from thefirst subset of images (e.g., based in part on the first route record),a respective plant size of each respective plant of a plurality ofplants in the agricultural plot. In some embodiments, the method(s)further comprise estimating, from the second subset of images (e.g.,based in part on the second route record), for each fruit of a pluralityof fruit growing in the canopy of the agricultural plot, a respectivefruit size.

In some embodiments, the first route record comprises i) a first routeover (or alternatively through) the agricultural plot and ii) arespective velocity of the first vehicle for each image in the firstplurality of images, and the second route record comprises i) a secondroute over (or alternatively through) the agricultural plot and ii) arespective velocity of the second vehicle for each image in the secondsubset of images.

In some embodiments, the second route comprises a subset (e.g., lessthan all) of plants (e.g., trees) growing in the agricultural plot. Insome embodiments, the second route comprises a subset of the area (e.g.,less than all) of the agricultural plot. In some such embodiments, theplurality of fruit comprises fruit growing on plants in a respectiveportion of the agricultural plot (e.g., fruits growing on the subset ofplants).

In some embodiments, the second subset of images of the canopy of theagricultural plot includes two or more images obtained at differentpositions in the agricultural plot, where the two or more images includea respective fruit growing in the agricultural plot. In someembodiments, the method further comprises determining from the secondsubset of images, for each fruit (e.g., individual instance of a fruit)of a plurality of fruit growing on a subset of plants, a respectivefruit size. In some embodiments, the method further comprisesdetermining from the fourth subset of images, for each fruit of aplurality of fruit growing on a subset of plants, a respective fruitsize.

In some embodiments, the subset of plants is predetermined (e.g.,corresponding to a predetermined route through the agricultural plot orcorresponding to one or more predetermined plants—e.g., referenceplants). In some embodiments, the subset of plants is determined asthose plants detected in the second and/or fourth subsets of images. Insome embodiments, the subset of plants is random (e.g., if the route isnot predetermined).

In some embodiments, the second and/or fourth subsets of images are usedto determine tree sizes or other tree-specific features (e.g., such astree size, tree health, etc.). In some embodiments, the first and/orthird subsets of images are used to determine plot-wide characteristics.In some embodiments, the second and/or fourth subsets of images compriseimages of a subset of the tress growing in the agricultural plot. Insome embodiments, the method(s) further comprise determining, from thesecond and/or fourth subsets of images, for each fruit of the pluralityof fruit growing on the plurality of plants, a respective fruit color.

In some embodiments, the method(s) further comprise determiningrespective fruit health for each fruit in the plurality of fruitdetected in the agricultural plot.

Referring to block 1224 in FIG. 12B, in some embodiments, the method(s)further comprise identifying a respective contour for each fruit in theone or more fruit.

In some embodiments, the determining further comprises, for eachrespective fruit of the plurality of fruit (e.g., for each fruitidentified in the second and/or fourth subset of images) using a firsttrained computational model applied to the two or more images thatinclude the respective fruit, identifying a corresponding contour of therespective fruit in the respective two or more images. In some suchembodiments, respective contours of the same fruit in the two or moreimages are thereby obtained. In some embodiments, the determiningfurther comprises calculating a size of the respective fruit from therespective contours of the respective fruit in the two or more images.

In some embodiments, a second trained computational model (e.g., avalidation model) is used to verify that each contour corresponds to afruit (e.g., rather than a leaf). In some embodiments, contours that donot correspond to a fruit are discarded.

In some embodiments, two or more contours are identified ascorresponding to a respective fruit based at least in part by matchingthe two or more contours based at least in part on one or morerespective features for each contour. In some embodiments, the one ormore respective features comprise color, texture, and/or shapedetermined for a corresponding contour (e.g., the color and texture forthe corresponding contour comprises a color and texture for an areabounded by the contour, whereas the shape determined for thecorresponding contour is the shape of the contour).

In some embodiments, calculating the respective fruit size for eachfruit in the plurality of fruit includes assigning a respective fruitidentifier for each fruit in the plurality of fruit, and averaging thesize of each respective fruit across the plurality of images.

In some embodiments, a respective fruit size is calculated via stereotriangulation using two or more images including contours correspondingto the respective fruit, as explained above. In some embodiments, arespective fruit size is not determined using a composite image (e.g.,fruit size is not determined by directly comparing contours for arespective fruit in two or images on a pixel-by-pixel basis).

Block 1226.

The method(s) further includes predicting a yield of fruit for theagricultural plot. Referring to block 1228, in one aspect of the presentdisclosure, the method comprises, using at least i) the first and secondnumber of fruit detectable from the aerial view of the canopy of theagricultural plot and ii) contextual information for the agriculturalplot, predicting the yield of fruit from the agricultural plot.Referring to block 1230, in another aspect of the present disclosure, insome embodiments, the method further includes predicting a yield offruit growing in the agricultural plot based at least in part on theplurality of fruit sizes. Referring to block 1232, in yet another aspectof the present disclosure, the method further includes using the numberof fruit detectable from the aerial view of the canopy of theagricultural plot and the plurality of fruit sizes, to predict the yieldof fruit from the agricultural plot.

In some embodiments, the method(s) further comprise obtaining historicalyield information for the agricultural plot, and scaling the estimate ofthe yield of fruit from the agricultural plot using the historical yieldinformation. In some embodiments, the scaling is non-linear. In someembodiments, historical yield information further comprises informationon historical fruit sizes, and respective fruit sizes are scaled usingthe historical yield information. In some embodiments, historical datacomprises fruit yield information from previous years. In someembodiments, historical data comprises historical data of a particularfruit type, historical data from a particular region (e.g., historicaldata for a plurality of agricultural plots), historical data for theagricultural plot, historical data for at least one differentagricultural plot, or any combination thereof.

In some embodiments, the method further comprises scaling the predictedyield of fruit from the agricultural plot by a plurality of fruit sizes(e.g., by a plurality of fruit sizes obtained from historical data). Insome embodiments, scaling the predicted yield of fruit by the pluralityof fruit sizes is non-linear.

In some embodiments, the method further comprises determining, for eachfruit in the plurality of fruit, a respective fruit weight based on thecorresponding fruit size, and scaling the predicted yield of fruit bythe plurality of fruit weights. For example, in some embodiments, thescaling comprises multiplying an estimated number of fruit by an averageweight for the plurality of fruit, thereby providing a predicted yieldin weight (e.g., tons) of fruit. In some embodiments, scaling thepredicted yield of fruit by the plurality of fruit weights isnon-linear.

In some embodiments, the method further comprises estimating arespective yield of fruit for each plant in the plurality of plants inthe agricultural plot (e.g., a per tree yield). In some embodiments,this estimating comprises scaling the predicted yield of fruit for theagricultural plot by the number of plants detected. In some embodiments,this estimating comprises scaling the predicted yield of fruit from theagricultural plot using the plurality of plant sizes. In someembodiments, this estimating comprises scaling the predicted yield offruit for the agricultural plot using at least the number of plantsdetected and the plurality of plant sizes. In some embodiments, thescaling is non-linear. In some embodiments, estimating a yield for eachplant growing in the agricultural plot comprises determining arespective fruit count for each plant.

In some embodiments, the method further comprises providing a predictedcount of fruit for the agricultural plot (e.g., based on an estimatedcount of fruit for each plant growing in the agricultural plot).

In some embodiments, the method further comprises displaying a histogramof the predicted yield of fruit. In some embodiments, this histogram isbased at least in part on fruit size using the fruit sizes for theplurality of fruit (e.g., the histogram provides a count or percentageof fruit for each respective fruit size bin). FIG. 4 provides an exampleof one such histogram. In some embodiments, this histogram is based atleast in part on plant size using the plant sizes for the plurality ofplants (e.g., the histogram provides a count or percentage of fruit foreach respective plant size bin, thereby providing information on therespective productivity of each plant size).

In some embodiments, the method is repeated at one or more time pointsover a season (e.g., monthly, weekly, etc.) to obtain updates of thepredicted yield of fruit.

In some embodiments, the method further comprises providing a confidencevalue for the predicted yield of fruit from the agricultural plot (e.g.,an accounting of the possible errors from estimated fruit sizes,estimated fruit counts, etc.).

In some embodiments, the method further comprises estimating for eachtree detected in the first plurality of images, a respective number offruit and, for each fruit growing in each tree a corresponding fruitsize. In some such embodiments, the method further comprises scaling thepredicted yield of fruit from the agricultural plot using the respectivenumber of fruit and the corresponding fruit sizes for each tree in thefirst plurality of images. In some embodiments, scaling the predictedyield of fruit by the respective number of fruit and the plurality offruit sizes is non-linear.

In some embodiments, respective plant sizes are estimated, usingtriangulation (as explained with reference to FIG. 8 ), from two or moreimages in the first plurality of images. In some embodiments,triangulation for determining plant size is based at least in part onthe velocities in the first route record. In some embodiments, theestimate of respective fruit sizes is performed using triangulation,based at least in part on the velocities in the second route record. Insome embodiments, respective plant health is determined for one or moreplants growing in the agricultural plot.

In some embodiments, information for the agricultural plot furthercomprises historical yield information for a region corresponding to theagricultural plot (e.g., the surrounding area). In some embodiments, theadditional information comprises current yield information for theregion corresponding to the agricultural plot.

In some embodiments, the method further comprises providing a userreport including fruit yield and/or other determined characteristics ofthe agricultural plot. In some embodiments, FIGS. 9, 10, and 11correspond to respective user reports.

Embodiments for Training a Computational Model to Identify FruitContours.

In some embodiments, a method of training a computational model toidentify fruit in agricultural plot images is provided. The method isperformed at a computer system (e.g., server system 502) having one ormore processors, and memory storing one or more programs for executionby the one or more processors. The method proceeds by obtaining, inelectronic format, a training dataset. The training dataset comprises aplurality of training images from a plurality of agricultural plots.

Each training image is from a respective agricultural plot in theplurality of agricultural plots and comprises at least one identifiedfruit. The method further includes determining, for each respectivefruit in each respective training image in the plurality of trainingimages, a corresponding contour, and a corresponding fruit size. Themethod further includes training an untrained or partially trainedcomputational model using at least the corresponding contour andcorresponding fruit size for each respective fruit in each respectivetraining image in the plurality of training images. The method therebyobtains a first trained computational model that is configured toidentify fruit in agricultural plot images. The first trainedcomputational model may be used in accordance with any of theembodiments described above.

In some embodiments, the first trained computational model is trainedbased at least in part on historical data comprising a plurality oftraining images obtained from a plurality of agricultural plots.

In some embodiments, the plurality of training images comprises at least10 training images, at least 20 training images, at least 50 trainingimages, at least 100 training images, at least 250 training images, atleast 500 training images, at least 1000 training images, at least 2500training images, or at least 5000 training images. In some embodiments,the plurality of training images is derived from a plurality ofagricultural plots.

In some embodiments, the plurality of agricultural plots comprises atleast 2 agricultural plots, at least 5 agricultural plots, at least 10agricultural plots, at least 20 agricultural plots, at least 50agricultural plots, or at least 100 agricultural plots. In someembodiments, each respective agricultural plot in the plurality ofagricultural plots has a corresponding plurality of training images.

In some embodiments, the untrained or partially trained computationalmodel (e.g., a machine learning model and/or an artificial intelligence(AI) model) is based on a neural network algorithm, a support vectormachine algorithm, a decision tree algorithm, an unsupervised clusteringalgorithm, a supervised clustering algorithm, or a logistic regressionalgorithm. In some embodiments, the untrained or partially trainedcomputational model is a multinomial classifier.

In some embodiments, the first trained computational model is updated(e.g., retrained) using at least one or more corrected fruit contours.In some embodiments, the first trained computational model is retrainedone or more times (e.g., on a second plurality of training images). Insome embodiments, one or more corrections are made to contours in one ormore images in the first plurality of training images, and the one ormore resulting contour corrections are used for retraining the trainedcomputational model. In some embodiments, the one or more contourcorrections are performed by a second trained computational model (e.g.,a contour validation model).

In some embodiments, the second trained computational model is based ona neural network algorithm, a support vector machine algorithm, adecision tree algorithm, an unsupervised clustering algorithm, asupervised clustering algorithm, or a logistic regression algorithm. Insome embodiments, the second trained computational model is amultinomial classifier.

In some embodiments, the first and/or second computational model is aneural network or a convolutional neural network. Examples of neuralnetworks are provided by Tajbakhsh et al. 2016 “Convolutional neuralnetworks for medical image analysis: Full training or fine tuning?” IEEETransactions on Medical Imaging 35(5), 1299-1312 and Larochelle et al.,2009, “Exploring strategies for training deep neural networks” J MachLearn Res 10, 1-40, each of which is hereby incorporated by reference.

In some embodiments, the first and/or second computational model is asupport vector machine (SVM). Example SVMs are described in Tong andChang 2001 “Support vector machine active learning for image retrieval”Proc. 9th ACM Int. Conf. Multimedia 107-118; Noble 2006 “What is asupport vector machine?” Nat. Biotech 24(12), 1565-1567; and Fung andMangasarian 2005 “Multicategory Proximal Support Vector MachineClassifiers” Mach. Learning 59, 77-97, each of which is herebyincorporated by reference in its entirety. When used for classification,SVMs separate a given set of binary-labeled data with a hyperplane thatis maximally distant from the labeled data. For cases in which no linearseparation is possible, SVMs can be used in combination with one or morekernels, which automatically realizes a non-linear mapping to a featurespace. The hyperplane determined by an SVM in feature space correspondsto a non-linear decision boundary in the input space.

In some embodiments, the first and/or second computational model is adecision tree or random forest. In some embodiments, the decision treeis random forest regression. Decision trees are described by Friedl andBrodley 1997 “Decision tree classification of land cover from remotelysensed data” Remote Sens. Environ. 61(3), 399-409, and random forestsfor analysis of image data are described in Gislason et al. 2005 “RandomForests for land cover classification” Pat. Recog. Let. 27, 294-300,which are hereby incorporated by reference. Tree-based methods partitionthe feature space into a set of rectangles, and then fit a model (e.g.,a constant) to each one.

Particular exemplary clustering techniques that can be used in thepresent disclosure include, but are not limited to, hierarchicalclustering (agglomerative clustering using nearest-neighbor algorithm,farthest-neighbor algorithm, the average linkage algorithm, the centroidalgorithm, or the sum-of-squares algorithm), k-means clustering, fuzzyk-means clustering algorithm, and Jarvis-Patrick clustering. In someembodiments, the clustering comprises unsupervised clustering where nopreconceived notion of what clusters should form when the training setis clustered is imposed.

In some embodiments, the first and/or second computational modelcomprises a regression model. In some embodiments, the computationalmodel makes use of a regression model disclosed in Hastie et al., 2001,The Elements of Statistical Learning, Springer-Verlag, New York, whichis hereby incorporated by reference in its entirety.

In some embodiments, a same computational model is used as both thefirst and second computational model. In some embodiments, the firstcomputational model is different from the second computational model.

Plural instances may be provided for components, operations, orstructures described herein as a single instance. Finally, boundariesbetween various components, operations, and data stores are somewhatarbitrary, and particular operations are illustrated in the context ofspecific illustrative configurations. Other functional allocations areenvisioned and may fall within the scope of the presently describedimplementation(s). In general, structures and functionality presented asseparate components in the example configurations may be implemented asa combined structure or component. Similarly, structures andfunctionality presented as a single component may be implemented asseparate components. These and other variations, modifications,additions, and improvements fall within the scope of theimplementation(s).

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first widget could be termed asecond widget, and, similarly, a second widget could be termed a firstwidget, without departing from the scope of the present disclosure. Thefirst subject and the second subject are both widgets, but they are notthe same widget.

The terminology used in the present disclosure is intended to describeparticular embodiments only and is not intended to be limiting of theinvention. As used in the description of the invention and the appendedclaims, the singular forms “a,” “an,” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises,” “comprising,” “including,”“includes,” “having,” “has,” “with,” or variants thereof when used inthis specification or claims, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting (thestated condition or event (” or “in response to detecting (the statedcondition or event),” depending on the context.

The foregoing description included example systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative implementations. For purposes of explanation,numerous specific details were set forth in order to provide anunderstanding of various implementations of the inventive subjectmatter. It will be evident, however, to those skilled in the art thatimplementations of the inventive subject matter may be practiced withoutthese specific details. In general, well-known instruction instances,protocols, structures, and techniques have not been shown in detail.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit the implementations to the precise forms disclosed. Manymodifications and variations are possible in view of the aboveteachings. The implementations were chosen and described in order tobest explain the principles and their practical applications, therebyenabling others skilled in the art to best utilize the implementationsand various implementations with various modifications as are suited tothe particular use contemplated.

What is claimed:
 1. A method of estimating a crop yield and crop size,comprising: obtaining, at a first location, a first image comprising aplurality of crop objects; obtaining, at a second location, a secondimage also comprising a plurality of crop objects; identifying, in thefirst image, first crop objects; identifying, in the second image,second crop objects; correlating the first crop objects with the secondcrop objects to determine a number of unique crop objects across thefirst and second images; and estimating a crop yield and a crop size,where the crop yield is based on a larger number of crop objects thanthe number of unique crop objects across the first and second images. 2.The method of claim 1, wherein the estimation of crop size includes anestimated distribution of crop sizes of the crop yield.
 3. The method ofclaim 2, further comprising: displaying, at a display of an electronicdevice, a histogram that includes a distribution of the crop sizes. 4.The method of claim 1, wherein the crop yield and crop size correspondto a particular volume of fruit.
 5. The method of claim 4, wherein thevolume of fruit is one of: a tree volume of an individual tree; and aplurality of trees in an agricultural plot.
 6. The method of claim 1,wherein: the first image is of a first area of an agricultural plot, andthe second image is of a second area that includes at least part of thefirst area; and the estimated crop yield is for at least theagricultural plot.
 7. The method of claim 6, where the first areacomprises at least part of a plant that grows the plurality of cropobjects.
 8. The method of claim 1, wherein the identifying of the firstand second crop objects is performed by a trained computational model.9. The method of claim 8, further comprising validating, via the trainedcomputational model, an aspect of the respective crop object.
 10. Themethod of claim 9, further comprising: in accordance with the first orsecond image failing to be validated by the trained computational model,using the first or second image to retrain the trained computationalmodel.
 11. The method of claim 1, further comprising, for eachrespective crop object identified in the first image and the secondimage, determining a size of the respective crop object based on a firstcontour of the respective crop object identified in the first image, anda second contour of the respective crop object identified in the secondimage.
 12. The method of claim 11, wherein the estimated crop yield isprovided as a weight or volume based on the size of each respective cropobject.
 13. The method of claim 11, wherein the first and secondcontours are distinct contours of the respective crop object.
 14. Themethod of claim 1, further comprising: determining that a respectivefirst crop object of the first crop objects and a respective second cropobject of the second crop objects are a same crop object based on one ormore respective features, selected from the group consisting of:contours of the respective first and second crop objects; colors of therespective first and second crop objects; textures of the respectivefirst and second crop objects; and shapes of the respective first andsecond crop objects.
 15. The method of claim 1, wherein estimating thecrop yield takes into account historical crop yield information for anagricultural plot where the first and second images were obtained. 16.The method of claim 1, wherein the first and second images are capturedby one or more of (i) ground-based cameras, and (ii) aerial cameras. 17.The method of claim 1, wherein the first image or the second image areobtained while the first crop objects or the second crop objects aregrowing in an agricultural plot.
 18. The method of claim 1, wherein: thefirst image is obtained at a first position; and the second image isobtained at a second position, wherein, the first and second positionsare different to one another.
 19. The method of claim 1, wherein: thefirst image is captured by a first camera; and the second image iscaptured by a second camera, distinct from the first camera.
 20. Themethod of claim 1, wherein the first image and the second image areobtained by a same camera.
 21. The method of claim 1, wherein each ofthe first crop objects and the second crop objects are fruit are of afruit type selected from the group consisting of blueberries, cherries,plums, peaches, nectarines, apricots, olives, mangos, pears, apples,quinces, loquats, citrus, figs, papayas, avocados, coconuts, durians,guavas, persimmons, and pomegranates.
 22. A computer system, comprising:one or more processors; and memory storing one or more programs, the oneor more programs including instructions for: obtaining, at a firstlocation, a first image comprising a plurality of crop objects;obtaining, at a second location, a second image also comprising aplurality of crop objects; identifying, in the first image, first cropobjects; identifying, in the second image, second crop objects;correlating the first crop objects with the second crop objects todetermine a number of unique crop objects across the first and secondimages; and estimating a crop yield and a crop size, where the cropyield is based on a larger number of crop objects than the number ofunique crop objects across the first and second images.
 23. Anon-transitory computer-readable storage medium storing one or moreprograms, which, when executed by a computer system with one or moreprocessors, cause the processors to perform a set of operations,comprising: obtaining, at a first location, a first image comprising aplurality of crop objects; obtaining, at a second location, a secondimage also comprising a plurality of crop objects; identifying, in thefirst image, first crop objects; identifying, in the second image,second crop objects; correlating the first crop objects with the secondcrop objects to determine a number of unique crop objects across thefirst and second images; and estimating a crop yield and a crop size,where the crop yield is based on a larger number of crop objects thanthe number of unique crop objects across the first and second images.